Principal Data Scientist

Quick Facts

• Role: Principal-level deep learning / AI leadership focused on graph and foundation models

Description

Lead the design and development of end-to-end deep learning solutions, from problem formulation and model architecture selection through deployment and continuous optimization within the Databricks Lakehouse platform. Serve as the technical authority for Graph Neural Networks (GNNs), graph representation learning, and advanced deep learning architectures, driving innovation and adoption across high-impact business problems. Architect scalable machine learning and deep learning platforms with reusable frameworks, standards, and best practices for model development, deployment, and lifecycle management.

Responsibilities

• Lead end-to-end deep learning solutions (problem formulation → architecture → deployment → continuous optimization) in the Databricks Lakehouse platform
• Act as technical authority for GNNs, graph representation learning, and advanced deep learning architectures
• Architect scalable ML/DL platforms and define reusable frameworks, standards, and best practices
• Define and implement evaluation, explainability, and model governance frameworks for quality, transparency, and business impact
• Lead development and optimization of distributed training, large-scale data processing, and inference systems using Spark and modern AI infrastructure
• Drive fine-tuning, adaptation, and productionization of foundation models, LLMs, and advanced AI architectures using enterprise data assets
• Partner with Data Science, Engineering, Product, and Business leaders to deliver scalable AI solutions with measurable value
• Influence AI strategy, technical roadmaps, and architectural decisions while mentoring senior practitioners and advancing organizational AI capabilities

Requirements

• PhD in Computer Science, Artificial Intelligence, Machine Learning, Applied Mathematics, Statistics, Operations Research, or a related quantitative field (strongly preferred)
• Master’s degree with exceptional equivalent industry research and leadership experience may be considered
• 12+ years developing and deploying ML and DL solutions in production environments
• 8+ years leading advanced deep learning research, experimentation, and enterprise-scale implementation efforts
• Demonstrated experience as a Principal Scientist/Principal AI Engineer/Distinguished Engineer/Research Lead or equivalent senior technical individual contributor
• Deep expertise in Deep Learning: GNNs, graph representation learning, graph embeddings, knowledge graphs, graph transformers, foundation models, and LLMs
• Expert-level hands-on proficiency in PyTorch and/or TensorFlow
• Extensive experience building large-scale distributed ML solutions using Apache Spark, PySpark, Databricks Lakehouse, Delta Lake, MLflow, and Unity Catalog
• Expert knowledge of model training, optimization, serving, and lifecycle management across distributed GPU environments
• Proven experience designing enterprise-grade inference systems balancing accuracy, latency, reliability, scalability, and operational cost
• Advanced proficiency in Python and ML ecosystems including NumPy, Pandas, Scikit-learn, PyTorch, and TensorFlow
• Strong publication record in leading ML/AI/graph ML/data science conferences and journals
• Proven record translating advanced research into measurable business outcomes
• Demonstrated technical leadership via patents, publications, conference presentations, open-source contributions, or recognized industry thought leadership

Benefits

• Competitive wages paid weekly
• Access to up to 50% of your earned wages before payday via Stream
• Associate discounts
• Health and financial well-being benefits for eligible associates (Medical, Dental, 401k and more!)
• Time off (vacation, holidays, sick pay)
• Leaders invested in training and career growth and development
• Inclusive work environment with talented colleagues